Cardiac arrhythmias involve abnormal electrical conduction or automatic changes in heart rhythm. Arrhythmias may vary in severity from those that are mild and require no treatment to those that are catastrophic and life threatening.
Most cardiac rhythm monitoring is performed to prevent death due to life-threatening cardiac arrhythmias (LTCA). However, current technology provides little more than detection and recognition of LTCA after it has started. This leaves very little time to protect the individual from death; the rhythm must be terminated within minutes or permanent neurologic damage or death will occur.
Only one method is in common use to predict an impending LTCA, namely, the frequency and complexity of premature ventricular complexes (PVCs). Our research, and that of others, suggests this method is unreliable. In the majority of patients the changes in frequency or complexity of PVCs do not correspond to the periods that precede initiation of LTCA. This failure probably accounts for its lack of commercial development.
Our results and those of other investigators demonstrate that the changes in RR-series can be a more accurate predictor of imminent LTCA than PVCs. However, the complexity and variability of RR-changes in different patients and even in the same patient during different periods of monitoring obscured application of this method for prediction of LTCA. Previous studies were focused on the detection of a single type of changes in the RR-series and did not allow to identify both linear and nonlinear changes. This diminished the accuracy of analysis, and made the results applicable to a small proportion of patients.
Frequency components of the RR-series contain physiologically important information about the activity of autonomic nervous system which, in turn, plays a major role in the initiation of LTCA. However, the nonstationary character of the signal affects the accuracy of spectral techniques. To overcome this problem, analysis based on Fast Fourier transform (FFT) or autoregressive modeling is usually performed over short and relatively stationary parts of the signal. Another approach uses the wavelet transform to decompose the signal into predefined frequency elements. However, neither method allows reliable identification of the nonstationary frequency elements that exhibit changes before LTCA. The analysis of short time windows requires stationarity of each portion of the signal, whereas the RR-series exhibits pronounced changes preceding LTCA. The wavelet transform decomposes the signal into constant frequency ranges, while individual RR-signals have highly variable frequency content.
Methods in general use include simple heart rate detection and frequency and, in some cases, repetitiveness of premature ventricular complexes (PVCs). The heart rate detector is set at high and low thresholds by the operator, and an alarm sounds if these are exceeded. The more sophisticated instruments also alarm when target thresholds for PVC frequency are exceeded. These are simple, primitive, inaccurate and ineffective. There is no method for predicting LTCA, only detection once they are in progress. Moreover, specificity for detection of significant arrhythmias is poor.
The linear changes before LTCA in the majority of patients (80-90%) are not different from those during the arrhythmia-free periods. Because these changes are not specifically associated with LTCA, in the majority of patients they cannot be used for the short-term prediction of arrhythmias. Conventional heart rate variability analysis in the frequency domain has revealed a complex pattern of changes but fails to identify specific changes that might predict LTCA as well. Moreover, the standard time (mean and standard deviation) and frequency (power spectrum) domain representations of a signal do not reveal the nonlinear changes that precede LTCA.
In animals and a small group of patients nonlinear methods based on different measures of complexity and deterministic chaos reveal low-dimensional excursions in the heart beat dynamics several hours before LTCA. Using point correlation dimension it was shown that the dynamics of heart rate over 12-24 hours before LTCA (ventricular fibrillation) exhibited low dimensional excursions in patients with ventricular fibrillation compared to the patients with similar clinical characteristics but without LTCA or to normal subjects. Enhanced nonlinear beat-to-beat alterations were observed 1 hour before the onset of LTCA in Poincare plots.
However, those methods do not allow extraction of the pattern, the onset, individual characteristics and the time course of changes. RR-changes are highly variable in different subjects and even in the same subject over different periods of time.
There is a need for a system for on-line, real-time electrocardiographic evaluation of patients that provides improved ability to assess the likelihood of an occurrence of LTCA. Reliable prediction of a potentially fatal event requires risk estimation to adjust treatment strategy.